Discovering potential settlements’ areas in Argentina

Let’s imagine there is a dataset with all the settlements in the world. You’ve got boundaries, name, population, administrative subordination. The dataset is being constantly updated, saving the information about the previous data. How beneficial it could be?

• Quick and targeted emergency response
• Exploring and modeling of worldwide urban growth
• Analyzing human footprint on climate change
• Creating regional planning strategies in developing countries
• Huge potential for data fusion in the fields of statistics, IoT, location intelligence, autonomous driving, AI and ML.

Obviously, the best place to organize such data would be the OpenStreetMap. Using OSM history and changesets databases researchers and developers would have access to history and the modernity of humanity’s progress.

Vision Team has challenged itself to lead the way in developing the Global Settlements Dataset.

This article describes the process of predicting potential settlements in Argentina. We have analyzed OpenStreetMap data, VIIRS night imagery, MaxMind World Cities, and developed a ready-to-use data to start a targeted campaign for adding settlements boundaries to OSM.

For information about the current state of settlements in Argentina see our post here.

• Image pre-processing
• Intersection
• Pitfalls
• Outcome
• Use case

IMAGE PRE-PROCESSING

We used data from the VIIRS spacecraft as the basis for the search project on potential settlements, missing on OpenStreetMap, as its night mode enables to capture in high accuracy objects emitting light. The spatial resolution of its sensor is 750 m.

The satellite image pre-processing procedure comprises 2 stages:

• Creating a binary mask on the threshold;
• Vectorization of the outcome result.

The image pre-processing was made with the gdal (gdal_calc and gdal_polygonize) tools.

INTERSECTION

Settlements in Argentina are mapped with a tag relation[“admin_level”=”8”].

For the further workflow, the data is uploaded via overpass-turbo.eu.

The binary mask should eliminate all area settlements already mapped on OSM.

Next step is intersection exclusion:

PITFALLS

The final mask showed two large clusters of light source, which turned not to include the settlements. During analysis, we discovered that the first source of the light was an oil derricks concentration cluster and the second — a power plant.

Similar to settlements, we have eliminated intersections with the objects, having the following tags:

• “industrial” = ”oil”
• “power” = ”generator”

While processing the data, we found out that many settlements are mapped partially, meaning areas are mapped without the boundaries of the cities. We have highlighted such cases in the project.

1.Contains node[“admin_level”=”8”]; 2.Contains[“land use”=”residential”]; 3. No valuable intersection;

Blue color means that the settlement, which we got on the basis of the satellite image has a point with the name of the city on OpenStreetMap. This means, that most likely this point is a real populated area.

Red color means a potential settlement which does not intersect with any object from OpenStreetMap.

Red outline means that the object has got intersections with residential areas [“land use” = ”residential”], but at the same time, has no mapped borders.

We included the settlements’ points from MaxMind, and excluded all the objects within a radius of 450 m from the points on OpenStreetMap.

USE CASE

Here is an example of how to apply the project data in searching settlements and mapping on OpenStreetMap.

So, we can see a wasteland on the map to the right

At the same time, in this area we found a potential area settlement and the point from MaxMind

After adding the WMS layer from Sentinel-2 satellite onto the given territory, one can see that the following area really has the settlement.

Our team is currently analyzing existing data on OpenStreetMap and other global open data projects. I’m preparing a dataset with potential settlements in the South America and moving towards the global coverage.

Check the result

We are encouraging the OSM community to join us in this initiative.

#GlobalSettlementsDataset is a huge step in the open data concept.